title: Human activities recognition with RGB-Depth camera using HMM.
abstract: Fall detection remains today an open issue for improving elderly people security. It is all the more pertinent today when more and more elderly people stay longer and longer at home. In this paper, we propose a method to detect fall using a system made up of RGB-Depth cameras. The major benefit of our approach is its low cost and the fact that the system is easy to distribute and install. In few words, the method is based on the detection in real time of the center of mass of any mobile object or person accurately determining its position in the 3D space and its velocity. We demonstrate in this paper that this information is adequate and robust enough for labeling the activity of a person among 8 possible situations. An evaluation has been conducted within a real smart environment with 26 subjects which were performing any of the eight activities (sitting, walking, going up, squatting, lying on a couch, falling, bending and lying down). Seven out of these eight activities were correctly detected among which falling which was detected without false positives.
authors.forename: Amandine
authors.initials: A
authors.lastname: Dubois
authors.forename: François
authors.initials: F
authors.lastname: Charpillet
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control; D000328:Adult; D000465:Algorithms; D004867:Equipment Design; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D011187:Posture; D014741:Video Recording / Q000295:instrumentation / Q000379:methods*; D016138:Walking; D055815:Young Adult